def plot_maps(plot_params, anat_fn, anat_slice_def, fig_dir,
orientation=['axial','sagittal'], crop_extension=None,
plot_anat=True, plot_fontsize=25, fig_dpi=75):
ldata = []
for p in plot_params:
c = xndarray.load(p['fn']).sub_cuboid(**p['slice_def'])
c.set_orientation(orientation)
ldata.append(c.data)
c_anat = xndarray.load(anat_fn).sub_cuboid(**anat_slice_def)
c_anat.set_orientation(orientation)
resolution = c_anat.meta_data[1]['pixdim'][1:4]
slice_resolution = resolution[MRI4Daxes.index(orientation[0])], \
resolution[MRI4Daxes.index(orientation[1])]
all_data = np.array(ldata)
if 'prl' in plot_params[0]['fn']:
norm = normalize(all_data.min(), all_data.max()*1.05)
print 'norm:', (all_data.min(), all_data.max())
else:
norm = normalize(all_data.min(), all_data.max())
print 'norm:', (all_data.min(), all_data.max())
for data, plot_param in zip(all_data, plot_params):
fn = plot_param['fn']
plt.figure()
print 'fn:', fn
print '->', (data.min(), data.max())
if plot_anat:
anat_data = c_anat.data
else:
anat_data = None
plot_func_slice(data, anatomy=anat_data,
parcellation=plot_param.get('mask'),
func_cmap=cmap,
parcels_line_width=1., func_norm=norm,
resolution=slice_resolution,
crop_extension=crop_extension)
set_ticks_fontsize(plot_fontsize)
fig_fn = op.join(fig_dir, '%s.png' %op.splitext(op.basename(fn))[0])
output_fig_fn = plot_param.get('output_fig_fn', fig_fn)
print 'Save to: %s' %output_fig_fn
plt.savefig(output_fig_fn, dpi=fig_dpi)
autocrop(output_fig_fn)
return norm
def plot_estimation_results(fig_dir, poi, jde_roi, cond, plot_label,
glm_fir_output_dir, rfir_output_dir,
jde_output_dir, ymin=-1.55, ymax=1.05,
plot_fontsize=25):
## HRF plots
fn = op.join(glm_fir_output_dir, 'glm_fir_hrf.nii.gz')
fir = xndarray.load(fn).sub_cuboid(condition=cond, **poi)
#fir /= (fir**2).sum()**.5
fir /= fir.max()
fn = op.join(rfir_output_dir, 'rfir_ehrf.nii.gz')
rfir = xndarray.load(fn).sub_cuboid(condition=cond, **poi)
#rfir /= (rfir**2).sum()**.5
rfir /= rfir.max()
fn = op.join(jde_output_dir, 'jde_mcmc_hrf_pm.nii.gz')
jde = xndarray.load(fn).sub_cuboid(ROI=jde_roi)
jde /= jde.max()
plt.figure()
pargs = {'linewidth' : 2.7}
plot_cub_as_curve(fir, show_axis_labels=False, plot_kwargs=pargs)
plot_cub_as_curve(rfir, show_axis_labels=False, plot_kwargs=pargs)
plot_cub_as_curve(jde, show_axis_labels=False, plot_kwargs=pargs)
from pyhrf.boldsynth.hrf import getCanoHRF
time_points, hcano = getCanoHRF()
hcano /= hcano.max()
plt.plot(time_points, hcano, 'k.-',linewidth=1.5)
set_ticks_fontsize(plot_fontsize)
plt.xlim(0,25)
plt.ylim(ymin, ymax)
plt.gca().xaxis.grid(True, 'major', linestyle='--', linewidth=1.2,
color='gray')
hrf_fig_fn = op.join(fig_dir, 'real_data_hrfs_%s.png' %plot_label)
print 'hrf_fig_fn:', hrf_fig_fn
plt.savefig(hrf_fig_fn)
autocrop(hrf_fig_fn)
def plot_detection_results(fig_dir, poi, condition, coi, parcellation_file,
plot_label, jde_output_dir, glm_hcano_rs_output_dir,
fig_dpi=100):
"""
coi (str): contrast of interest
poi (dict): defines the point of interest for plots of HRFs and maps
"""
if condition == 'audio':
condition = 'phraseaudio'
orientation = ['coronal', 'sagittal']
axial_slice = poi['axial']
anat_file = get_data_file_name('real_data_vol_4_regions_anatomy.nii.gz')
parcellation = xndarray.load(parcellation_file)
parcellation = parcellation.sub_cuboid(axial=axial_slice)
parcellation = parcellation.reorient(orientation)
## Detection maps
detection_plots_params = []
#JDE NRLs
fn = op.join(jde_output_dir, 'jde_mcmc_nrl_pm.nii.gz')
slice_def = {'axial':axial_slice, 'condition':condition}
fig_fn = op.join(fig_dir, 'real_data_jde_mcmc_nrls_%s.png' %condition)
detection_plots_params.append({'fn':fn, 'slice_def':slice_def,
'mask': parcellation.data,
'output_fig_fn':fig_fn})
#GLM hcano
fn = op.join(glm_hcano_rs_output_dir, 'glm_hcano_rs_beta_%s.nii.gz' %condition)
slice_def = {'axial':axial_slice}
fig_fn = op.join(fig_dir, 'real_data_glm_hcano_rs_%s.png'%condition)
detection_plots_params.append({'fn':fn, 'slice_def':slice_def,
'mask': (parcellation.data != 0),
'output_fig_fn':fig_fn})
perf_norm = plot_maps(detection_plots_params, anat_file,
{'axial':axial_slice*3},
fig_dir, orientation=orientation,
crop_extension=None, plot_anat=True)
palette_fig_fn = op.join(fig_dir, 'real_data_detection_%s_palette.png' \
%condition)
plot_palette(cmap, perf_norm, 45)
plt.savefig(palette_fig_fn, dpi=fig_dpi)
autocrop(palette_fig_fn)
#JDE Contrast
fn = op.join(jde_output_dir, 'jde_mcmc_nrl_contrasts.nii.gz')
slice_def = {'axial':axial_slice, 'contrast':coi}
fig_fn = op.join(fig_dir, 'real_data_jde_mcmc_con_%s.png' %coi)
detection_plots_params.append({'fn':fn, 'slice_def':slice_def,
'mask': parcellation.data,
'output_fig_fn':fig_fn})
#GLM hcano
fn = op.join(glm_hcano_rs_output_dir, 'glm_hcano_rs_con_effect_%s.nii.gz'%coi)
slice_def = {'axial':axial_slice}
fig_fn = op.join(fig_dir, 'real_data_glm_hcano_rs_con_%s.png' %coi)
detection_plots_params.append({'fn':fn, 'slice_def':slice_def,
'mask': (parcellation.data != 0),
'output_fig_fn':fig_fn})
perf_norm = plot_maps(detection_plots_params, anat_file,
{'axial':axial_slice*3},
fig_dir, orientation=orientation,
crop_extension=None, plot_anat=True)
palette_fig_fn = op.join(fig_dir, 'real_data_detection_con_%s_palette.png'
%coi)
plot_palette(cmap, perf_norm, 45)
plt.savefig(palette_fig_fn, dpi=fig_dpi)
autocrop(palette_fig_fn)